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search.filters.applied.f.access: openAccess × Author: Cornell, Sarah E. × DDC: 550 × Has files: Yes × Publisher: Göttingen : Copernicus Publ. × WGL Institute: PIK ×

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    Earth system data cubes unravel global multivariate dynamics
    (Göttingen : Copernicus Publ., 2020) Mahecha, Miguel D.; Gans, Fabian; Brandt, Gunnar; Christiansen, Rune; Cornell, Sarah E.; Fomferra, Normann; Kraemer, Guido; Peters, Jonas; Bodesheim, Paul; Camps-Valls, Gustau; Donges, Jonathan F.; Dorigo, Wouter; Estupinan-Suarez, Lina M.; Gutierrez-Velez, Victor H.; Gutwin, Martin; Jung, Martin; Londoño, Maria C.; Miralles, Diego G.; Papastefanou, Phillip; Reichstein, Markus
    Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
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    Resolving ecological feedbacks on the ocean carbon sink in Earth system models
    (Göttingen : Copernicus Publ., 2021) Armstrong McKay, David I.; Cornell, Sarah E.; Richardson, Katherine; Rockström, Johan
    The Earth's oceans are one of the largest sinks in the Earth system for anthropogenic CO2 emissions, acting as a negative feedback on climate change. Earth system models project that climate change will lead to a weakening ocean carbon uptake rate as warm water holds less dissolved CO2 and as biological productivity declines. However, most Earth system models do not incorporate the impact of warming on bacterial remineralisation and rely on simplified representations of plankton ecology that do not resolve the potential impact of climate change on ecosystem structure or elemental stoichiometry. Here, we use a recently developed extension of the cGEnIE (carbon-centric Grid Enabled Integrated Earth system model), ecoGEnIE, featuring a trait-based scheme for plankton ecology (ECOGEM), and also incorporate cGEnIE's temperature-dependent remineralisation (TDR) scheme. This enables evaluation of the impact of both ecological dynamics and temperature-dependent remineralisation on particulate organic carbon (POC) export in response to climate change. We find that including TDR increases cumulative POC export relative to default runs due to increased nutrient recycling (+∼1.3 %), whereas ECOGEM decreases cumulative POC export by enabling a shift to smaller plankton classes (−∼0.9 %). However, interactions with carbonate chemistry cause opposite sign responses for the carbon sink in both cases: TDR leads to a smaller sink relative to default runs (−∼1.0 %), whereas ECOGEM leads to a larger sink (+∼0.2 %). Combining TDR and ECOGEM results in a net strengthening of POC export (+∼0.1 %) and a net reduction in carbon sink (−∼0.7 %) relative to default. These results illustrate the degree to which ecological dynamics and biodiversity modulate the strength of the biological pump, and demonstrate that Earth system models need to incorporate ecological complexity in order to resolve non-linear climate–biosphere feedbacks.